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Pappagallo, A.; Massini, A.; Tronci, E. |
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Title |
Monte Carlo Based Statistical Model Checking of Cyber-Physical Systems: A Review |
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2020 |
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Information |
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11 |
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558 |
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MCLab @ davi @ |
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181 |
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Tortora, L.; Meynen, G.; Bijlsma, J.; Tronci, E.; Ferracuti, S. |
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Title |
Neuroprediction and A.I. in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective |
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Journal Article |
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Year |
2020 |
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Frontiers in Psychology |
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11 |
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220 |
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Advances in the use of neuroimaging in combination with A.I., and specifically the use of machine learning techniques, have led to the development of brain-reading technologies which, in the nearby future, could have many applications, such as lie detection, neuromarketing or brain-computer interfaces. Some of these could, in principle, also be used in forensic psychiatry. The application of these methods in forensic psychiatry could, for instance, be helpful to increase the accuracy of risk assessment and to identify possible interventions. This technique could be referred to as ‘A.I. neuroprediction,’ and involves identifying potential neurocognitive markers for the prediction of recidivism. However, the future implications of this technique and the role of neuroscience and A.I. in violence risk assessment remain to be established. In this paper, we review and analyze the literature concerning the use of brain-reading A.I. for neuroprediction of violence and rearrest to identify possibilities and challenges in the future use of these techniques in the fields of forensic psychiatry and criminal justice, considering legal implications and ethical issues. The analysis suggests that additional research is required on A.I. neuroprediction techniques, and there is still a great need to understand how they can be implemented in risk assessment in the field of forensic psychiatry. Besides the alluring potential of A.I. neuroprediction, we argue that its use in criminal justice and forensic psychiatry should be subjected to thorough harms/benefits analyses not only when these technologies will be fully available, but also while they are being researched and developed. |
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1664-1078 |
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MCLab @ davi @ Neuroprediction-2020 |
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180 |
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Mancini, T.; Mari, F.; Massini, A.; Melatti, I.; Salvo, I.; Sinisi, S.; Tronci, E.; Ehrig, R.; Röblitz, S.; Leeners, B. |
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Computing Personalised Treatments through In Silico Clinical Trials. A Case Study on Downregulation in Assisted Reproduction |
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2018 |
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25th RCRA International Workshop on “Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion” (RCRA 2018) |
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MCLab @ davi @ |
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175 |
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Mancini, T.; Tronci, E.; Scialanca, A.; Lanciotti, F.; Finzi, A.; Guarneri, R.; Di Pompeo, S. |
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Title |
Optimal Fault-Tolerant Placement of Relay Nodes in a Mission Critical Wireless Network |
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Conference Article |
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2018 |
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25th RCRA International Workshop on “Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion” (RCRA 2018) |
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MCLab @ davi @ |
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174 |
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Author |
Driouich, Y.; Parente, M.; Tronci, E. |
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Model Checking Cyber-Physical Energy Systems |
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Conference Article |
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2018 |
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Proceedings of 2017 International Renewable and Sustainable Energy Conference, IRSEC 2017 |
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Institute of Electrical and Electronics Engineers Inc. |
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MCLab @ davi @ Driouich2018 |
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177 |
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Mancini, T.; Mari, F.; Melatti, I.; Salvo, I.; Tronci, E.; Gruber, J.; Hayes, B.; Prodanovic, M.; Elmegaard, L. |
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Title |
Parallel Statistical Model Checking for Safety Verification in Smart Grids |
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Conference Article |
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2018 |
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2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) |
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1-6 |
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MCLab @ davi @ mancini-etal:2018:smartgridcomm |
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170 |
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Driouich, Y.; Parente, M.; Tronci, E. |
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A methodology for a complete simulation of Cyber-Physical Energy Systems |
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Conference Article |
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2018 |
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EESMS 2018 – Environmental, Energy, and Structural Monitoring Systems, Proceedings |
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1-5 |
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MCLab @ davi @ Driouich20181 |
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169 |
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Della Penna, Giuseppe; Tofani, Alberto; Pecorari, Marcello; Raparelli, Orazio; Intrigila, Benedetto; Melatti, Igor; Tronci, Enrico |
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A Case Study on Automated Generation of Integration Tests |
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2006 |
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Fdl |
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278-284 |
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Ecsi |
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978-3-00-019710-9 |
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yes |
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Sapienza @ mari @ Dtprimt06 |
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27 |
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Cavaliere, Federico; Mari, Federico; Melatti, Igor; Minei, Giovanni; Salvo, Ivano; Tronci, Enrico; Verzino, Giovanni; Yushtein, Yuri |
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Title |
Model Checking Satellite Operational Procedures |
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Conference Article |
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2011 |
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DAta Systems In Aerospace (DASIA), Org. EuroSpace, Canadian Space Agency, CNES, ESA, EUMETSAT. San Anton, Malta, EuroSpace. |
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We present a model checking approach for the automatic verification of satellite operational procedures (OPs). Building a model for a complex system as a satellite is a hard task. We overcome this obstruction by using a suitable simulator (SIMSAT) for the satellite. Our approach aims at improving OP quality assurance by automatic exhaustive exploration of all possible simulation scenarios. Moreover, our solution decreases OP verification costs by using a model checker (CMurphi) to automatically drive the simulator. We model OPs as user-executed programs observing the simulator telemetries and sending telecommands to the simulator. In order to assess feasibility of our approach we present experimental results on a simple meaningful scenario. Our results show that we can save up to 90% of verification time. |
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yes |
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Sapienza @ mari @ Dasia11 |
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13 |
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Mari, Federico; Melatti, Igor; Salvo, Ivano; Tronci, Enrico |
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From Boolean Relations to Control Software |
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Conference Article |
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2011 |
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Proceedings of ICSEA 2011, The Sixth International Conference on Software Engineering Advances |
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528-533 |
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Many software as well digital hardware automatic synthesis methods define the set of implementations meeting the given system specifications with a boolean relation K. In such a context a fundamental step in the software (hardware) synthesis process is finding effective solutions to the functional equation defined by K. This entails finding a (set of) boolean function(s) F (typically represented using OBDDs, Ordered Binary Decision Diagrams) such that: 1) for all x for which K is satisfiable, K(x, F(x)) = 1 holds; 2) the implementation of F is efficient with respect to given implementation parameters such as code size or execution time. While this problem has been widely studied in digital hardware synthesis, little has been done in a software synthesis context. Unfortunately the approaches developed for hardware synthesis cannot be directly used in a software context. This motivates investigation of effective methods to solve the above problem when F has to be implemented with software. In this paper we present an algorithm that, from an OBDD representation for K, generates a C code implementation for F that has the same size as the OBDD for F and a WCET (Worst Case Execution Time) linear in nr, being n = |x| the number of input arguments for functions in F and r the number of functions in F. |
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ThinkMind |
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978-1-61208-165-6 |
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Best Paper Award |
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yes |
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Sapienza @ mari @ icsea11 |
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14 |
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